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Ya Wang,1,* Jiajia Zhang,2,* Xiaoyan Chen,1,* Min Sun,3 Yanqing Li,4 Yanan Wang,5 Yan Gu,6 Yinyin Cai1 1Neurosurgical Intensive Care Unit, Department of Neurosurgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People’s Republic of China; 2Neurosurgery Section Two, Department of Neurosurgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People’s Republic of China; 3Department of Geriatrics Section Three, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People’s Republic of China; 4Department of Neurosurgery, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People’s Republic of China; 5Respiratory and Critical Care Medicine Intensive Care Unit, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People’s Republic of China; 6Infection Management Office, Affiliated Hospital of Nantong University, Nantong, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaoyan Chen, Email CXYtdfy@126.comObjective: To develop a predictive model for assessing the risk of multidrug-resistant organisms (MDROs) infection and validate its effectiveness.We conducted a study on a total of 2516 patients admitted to the neurosurgery intensive care unit (NICU) of a Grade-III hospital in Nantong City, Jiangsu Province, China, between January 2014 and February 2022. Patients meeting the inclusion criteria were selected using convenience sampling. The patients were randomly divided into modeling and validation groups in a 7:3 ratio. To address the category imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to adjust the MDROs infection ratio from 203:1558 to 812:609 in the training set. Univariate analysis and logistic regression analysis were performed to identify risk factors associated with MDROs infection in the NICU. A risk prediction model was developed, and a nomogram was created. Receiver operating character